System and method for optimizing paid search advertising campaigns based on natural search traffic

ABSTRACT

The invention is a computer-implementable methodology for quantitatively assessing the impact of paid search advertising on the total visits (paid and organic search-based visits) from search engines and utilizing this assessment to improve search engine marketing performance. The methodology relies on the computation of a synergy score for each search engine keyword of interest or coefficients in a synergy equation. Once computed, the score can be used to repeatedly compute the total return on advertising spend (ROAS) and other performance metrics on a go-forward basis without the time lag inherent in computing the synergy score itself. The invention includes specific computer-implementable methods for improving search engine marketing performance based on the total (paid plus organic) performance metrics.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application relates to and incorporates by reference U.S. patentapplication Ser. No. 11,678,002, entitled “System and Method forManaging Network-Based Advertising Conducted by Channel Partners of anEnterprise,” filed on Feb. 7, 2007, U.S. patent application Ser. No.11/689,406, entitled, “Centralized Web-Based Software Solutions ForSearch Engine Optimization,” filed on Mar. 21, 2007, U.S. patentapplication Ser. No. 11/689,414, entitled “A System and Method forMeasuring the Effectiveness of an On-Line Advertisement Campaign,” filedon Mar. 21, 2007, U.S. patent application Ser. No. 11/758,592, entitled“System And Method For Modeling Value Of An On-Line AdvertisementCampaign,” filed on Jun. 5, 2007.

FIELD OF THE INVENTION

The invention is directed to, among other things, methods and systemsfor modeling and optimizing the effectiveness of a paid searchadvertising campaign. In particular, but not by way of limitation,aspects of the invention pertain to one or more centralized web-basedsoftware solutions that measure the effects of a change to a paid searchadvertising campaign.

BACKGROUND OF THE INVENTION

When a user wishes to find information on the World Wide Web, he or shemay enter a query in a search engine. In response to each query, thesearch engine may return two types of results: organic (also known asnatural) search results and paid search results. Organic search resultsare those listings that the search engine shows without directcompensation from a third party. Paid search results are advertisementsthat are only shown so long as the advertiser pays the search engine.Paid search results are often labeled as “sponsored ads,” “sponsoredlinks” or “sponsored results.” The paid search results often appearadjacent to or above the organic search results, but may appear anywhereon the search engine results page (SERP). By way of example, FIG. 1 andFIG. 2 show two SERPs, including organic and paid results

Search engines provide each advertiser with a great deal of control overwhere the advertisers' ads appear and where they do not appear.Returning to FIG. 1, the company “United Auto Body and Paint” may bid onsearch terms such as “united,” “automobile,” “collision repair” and/or“united auto body and paint”. Many search engines allow the advertiserto pay a greater amount per impression or per click on an advertisementin order for the advertisement to appear in a better position on thepage (e.g., a higher position in the list of paid search results). Theadvertiser can specify that each ad be shown to users located anywherein the world or restricted to specific geographic regions, such as onlyusers in San Diego, Calif.

Due to finite budgets, no advertiser can afford to have their listingappear on every SERP. Instead, advertisers typically examine the actionstaken by users and only show their ads in scenarios where the return onadvertising spend (ROAS) is sufficient. The ROAS is defined as the valueof the actions (such as purchasing a product, viewing a webpage, ordownloading a white paper) taken by users as a result of a set ofadvertisements divided by the cost of those advertisements. Similarmetrics include return on investment (ROI), cost per acquisition (CPA),cost per success event, cost per value point, and expense to revenueratio (E/R). Data about the number of impressions served of each searchadvertisement and the cost of these advertisements is obtained from thesearch engines such as Google, Yahoo, and MSN. Data about the actionstaken by users (conversion data) is obtained from “Web analytics”systems that track usage of the advertiser website. Leading Webanalytics products include Coremetrics, Google Analytics, OmnitureSiteCatalyst, Unica NetInsight, and WebTrends Marketing Lab.

Currently, if a user clicks on an advertisement and then takes actionson the advertiser's webpage, most advertisers attribute the value of theactions taken to that advertisement. Returning to FIG. 2, if a userclicks on the “Economist.com/subscribe” advertisement and purchases asubscription on the target website, The Economist probably considersthis revenue to result from the advertisement. Supposing that TheEconomist pays $1 per click on the advertisement, 10% of users who clickon the advertisement actually purchase a subscription, and TheEconomist's net income per additional subscription is $40, The Economistwould calculate the ROAS as 10%*$40/$1=4.0. However, a basic premise ofthe above calculation is flawed because the revenue does not necessarilyresult from the advertisement.

Therefore, it would be advantageous to understand the true value of anadvertisement and to communicate that value to an advertiser.

Moreover, assessment of the impact of various advertising campaigns andprograms such as television, magazine, online display ads, and searchengine ads is commonly carried out via user surveys and other techniquessuch as marketing mix modeling. These techniques may be able to assessthe effects of major campaigns on a company's key performance metricssuch as revenue or website visits, but they cannot handle more granularmarketing actions, such as the impact of showing an ad in response to aparticular search engine query. Marketing mix modeling typicallyutilizes two to three years of historical data in the statisticalanalysis and is generally not used to assess the effect of a routinemarketing change implemented for a period of less than one day to twoweeks. Finally, surveys and marketing mix modeling both rely heavily onhuman expertise and custom analysis and do not appear to be well suitedto automation.

Therefore, it would be advantageous to automate the assessment of theimpacts of various advertising campaigns and programs. Moreover, itwould be advantageous to assess impacts of various advertising campaignsusing smaller data sets and/or real-time or recent data.

SUMMARY

Exemplary embodiments of the invention that are shown in the drawingsare summarized below. These and other embodiments are more fullydescribed in the Detailed Description section. It is to be understood,however, that there is no intention to limit the invention to the formsdescribed in this Summary of the Invention or in the DetailedDescription. One skilled in the art can recognize that there arenumerous modifications, equivalents and alternative constructions thatfall within the spirit and scope of the invention as expressed in theclaims.

In one aspect, the invention provides a system and method fordetermining an impact of a change to a paid search advertising campaign.The inventive systems and methods include certain embodiments thatidentify a change associated with the paid search advertising campaignand determine one or more effects resulting from the change. The one ormore effects are processed after they are identified. The processingresults in a synergy score, which is stored in memory. In relation toanother aspect of the invention, certain embodiments generate amathematical model for determining an estimated synergy. In relation toyet another aspect of the invention, certain embodiments determine anadjustment to be made to the paid search advertising campaign based onthe calculated synergy score, the mathematical model or both thecalculated synergy score and the mathematical model. Additional aspectsare further described in the detailed description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Various objects and advantages and a more complete understanding of theinvention are apparent and more readily appreciated by reference to thefollowing Detailed Description and to the appended claims when taken inconjunction with the accompanying Drawings wherein:

FIG. 1 shows a typical search engine results page for Google;

FIG. 2 shows a typical search engine results page for Yahoo;

FIG. 3 depicts a process flow diagram illustrating steps taken by asoftware solution in accordance with at least one embodiment of theinvention;

FIG. 4 shows a pictorial representation of paid and organic websitetraffic over a period of approximately ten weeks in accordance with atleast one embodiment of the invention;

FIG. 5 shows a table representative of a portion of data gathered inaccordance with at least one embodiment of the invention;

FIG. 6 shows a linear fit to a plot of organic and paid searchclick-through rates in accordance with at least one embodiment of theinvention;

FIG. 7 shows data sources used during the creation of a predictive modelfor estimating a synergy score in accordance with at least oneembodiment of the invention;

FIG. 8 shows data sources used during the calculation of an estimatedsynergy score in accordance with at least one embodiment of theinvention;

FIG. 9 depicts a process for computing a total value metric inaccordance with at least one embodiment of the invention;

FIG. 10 illustrates a user interface for sorting keywords based on aperformance metric that may be presented to a user in accordance with atleast one embodiment of the invention;

FIG. 11 shows a block diagram depicting a network system 1100 forcalculating a synergy score, determining a predictive model forestimating a synergy score and optimizing a paid search advertisingcampaign in accordance with at least one embodiment of the invention;and

FIG. 12 shows a block diagram depicting an alternative system forcalculating a synergy score, determining a predictive model forestimating a synergy score and optimizing a paid search advertisingcampaign in accordance with at least one embodiment of the invention.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Embodiments of the present invention include methodologies and systemsfor quantitatively assessing the impact of paid search advertising onthe total visits (paid and organic visits) from search engines andutilizing this assessment to improve search engine marketingperformance. Certain embodiments rely on the computation of a synergyscore for select search engine keywords of interest or coefficients in asynergy equation. This computation may utilize historical data from agiven time period (e.g., less than one day to two weeks). Once computed,the score can be used to repeatedly compute the total ROAS and otherperformance metrics on a go-forward basis without the time lag inherentin computing the synergy score itself.

A closer look at FIG. 2 indicates that the basic premise of the typicalROAS calculation described previously in the Background section isflawed because the revenue does not necessarily result from theadvertisement in the paid listings 120. Instead, the user could clickany of several other links appearing on the SERP and still purchase asubscription to The Economist. For example, the user could click on theorganic listing 110 of the website. Thus, if The Economist chose not topurchase the search ad, a substantial fraction of the traffic andrevenue currently captured by the ad would probably shift to organicsearch listing 110. Similarly, in FIG. 1 it is conceivable that thewww.UnitedAutoBodyAndPaint.com sponsored link improves the awareness ofthis business and drives additional traffic to thewww.unitedautobodyandpaint.com organic listing. Correctly calculatingROAS (or any other value metric) requires assessing the impact of theadvertisement on the advertiser's overall traffic and revenue, not juston traffic via the paid link itself.

When considering traffic to all of the links appearing on the SERP, itis helpful to recognize that these links include links to websitesoperated by the advertiser, affiliates of the advertiser (third partieswho direct some of their visitors to the advertiser's websites for afee), resellers of the advertiser's products or services, competitors ofthe advertiser and entities unrelated to the advertiser (e.g., the query“united” may return links to United Airlines, which has nothing to dowith United Auto Body and Paint). The links also include links toinformation sources (e.g., Wikipedia), which may or may not containlinks to the websites operated by the advertiser. Any of these links mayoccur as advertisements or appear organically. Finally, in addition toclicking on one or more of the links, the user has the option of notclicking on any link.

An advertiser's Web analytics system tracks and distinguishes betweenvisitors arriving at the advertiser's websites via paid and organiclinks. The Web analytics system records which search engine the visitorwas using, what their query into the search engine was, what landingpage the link took them to, and (if tracking codes are configuredappropriately in the search engine's advertising interface) whatcreative was shown for a paid ad on which the user clicked. The Webanalytics system also tracks traffic from affiliates' websites andinformation sources.

Certain embodiments of the invention compute a synergy score orcoefficients in a synergy equation. Certain embodiments may additionallygenerate or alternatively generate holistic performance metrics similarto the “paid only” metrics that search engine marketers use today, andthen use those metrics to optimize search engine marketing performance.

Computation of the Synergy Score or Synergy Equation

A synergy score quantifies the impact of paid search advertisements ontraffic levels resulting from organic search listings and/or the value(e.g., revenue) associated with that traffic.

The synergy score can be defined and scaled in numerous ways. In oneembodiment, the synergy score is calculated on a linear scale. A synergyscore of 0.0 indicates that changes in paid search advertising have noimpact on traffic via organic search listings. A synergy score of −1.0indicates that changing the paid search advertising to attract Xadditional visitors (or Y revenue or some other value metric) via paidsearch listings has an equal and opposite effect on the same valuemetric (e.g., additional visitors, revenue, etc.) for organic searchlistings. For example, the change reduces the number of visitors viaorganic search listings by X. A synergy score of 1.0 indicates thatchanging the paid search advertising to attract X additional visitorsvia the paid search listing also attracts an additional X visitors viaorganic search listings. A synergy score of −0.5 means that changing thepaid search advertising to attract X additional visitors via the paidsearch listing reduces the number of visitors via the organic searchlistings by 0.5*X. This score can then be used to evaluate and/or modifyadvertising campaigns.

In this and other embodiments, a different method of scoring may bedefined without changing the fundamental definition and characteristicof a synergy score as quantifying the impact of paid searchadvertisements on traffic or revenue from organic search listings of awebsite at one or more search engines.

For each advertiser, a different synergy score may be determined foreach search engine query and in relation to each search engine. Forexample, the synergy score for The Economist advertising on the searchterm “economist” would probably be different on Google search than onYahoo! search. Also, the synergy score for The Economist advertising onthe search term “economist” on Google would be different than thesynergy score for The Economist advertising on the search term “AlanGreenspan” on Google.

In addition, a different synergy score may be determined for differententities using the same search term. For example, the synergy score forThe Economist advertising on the search term “economist” on Google wouldbe different than the synergy score for United Auto Body and Paintadvertising on the search term “economist” on Google.

Synergy Score Calculation

As stated above, the synergy score can be calculated in various waysbased of various value metrics. In a first embodiment, as shown in FIG.3, the synergy score is calculated in terms of website traffic.

During a first period of time (Stage 310), data is collected before achange is made to a paid search engine campaign. As shown in FIG. 3, thedata describes a volume of paid search traffic and a volume of organicsearch traffic to a website via a first search engine (or multiplesearch engines). The website traffic can be measured by the advertiser'sWeb analytics system.

In Stage 320, a change is made to a paid search advertising campaign.Possible changes may include starting or stopping advertising on aparticular query or set of queries, increasing or decreasing the maximumcost per click bid on a particular query or set of queries, increasingor decreasing the daily budget limit on a particular query or set ofqueries and increasing or decreasing the number of hours per day that isshown (“dayparting”) on a particular query or set of queries. One ofskill in the art will appreciate alternative changes, including changesthat affect the paid search ad cost, visibility and/or traffic via aquery or queries of interest.

By way of example, FIG. 4 shows the effect of stopping advertising on aparticular query. When the paid advertisement is removed, the paidtraffic drops to zero and the organic traffic jumps by approximately thesame amount as the drop in paid traffic, corresponding to a synergyscore of approximately −1.0. Note the high degree of variability ofsearch traffic volume for this query day-over-day. Much of this is dueto a weekly cycle (lower traffic on weekends), but this does not fullyexplain the observed variability.

During a second period of time (Stage 330), additional data is collectedafter the change is made to the paid search engine campaign. Theadditional data indicates a new volume of paid search traffic and a newvolume of organic search traffic to the website via the first searchengine. As with the previous traffic data, the website traffic can bemeasured by the advertiser's Web analytics system. Traffic could bemeasured for the same length of time in each of the two states.

In Stage 340, the synergy score is determined. This may be done by usingthe following equation: (T_(O2)−T_(O1))/(T_(P2)−T_(P1)), where T_(O1) isthe organic traffic during the first period of time (before the change),T_(O2) is the organic traffic during the second period of time (afterthe change), T_(P1) is the paid traffic in state one and T_(P2) is thepaid traffic in state two.

One of skill in the art will appreciate alternative embodiments whereone or more stages of FIG. 3 are omitted or rearranged, and whereadditional stages are included. For example, it is possible that otherfactors beyond the change in the paid search advertising campaign affectwebsite traffic via a search engine query of interest. For example, inFIG. 4 the weekly cycle (lower traffic on weekends) explains a greateramount of day-to-day organic traffic change than is caused by pausing apaid search advertising campaign. A great improvement is to cycle thechange (e.g. by displaying/not displaying an ad on alternate days for aperiod of two weeks). This assigns each state to each weekday with equalfrequency. It also averages long-term trends (seasonality, effects of anoff-line advertising campaign, among others) across the two statesbetter than would be achieved by operating in state one for sevenconsecutive days and in state two for seven consecutive days.

Search engine query volume data, like that used in the above embodiment,can also include data regarding how often a query was entered on eachsearch engine in each time period. Such data is available from searchengines as the “impressions” value in performance reports foradvertisements that were “in market” 100% of the time. Alternatively, itis available in some scenarios via Google's Keyword Tool, Google Trends,and third-party data vendors such as Nielsen and comScore. Anotheralternative is to model the search engine query volume of the query ofinterest as a function of organic website traffic volume for queriesthat are not affected by the change in the paid search marketingprogram.

In a second embodiment, an advertiser's organic and paid search clickthrough rates (CTRs), CTR_(O) and CTR_(P), are defined as the numbers ofwebsite visits via organic and paid search links, respectively, inresponse to the query of interest divided by the search engine queryvolume for the query of interest. This definition is query-centricrather than ad-centric. Any time a paid ad is not shown, it will not beclicked on, and, based on the above definition, its CTR drops. This isdifferent from the ad-centric CTR provided for paid search ads by mostsearch engines, which is computed based on only those queries for whichthe ad was shown. This data may be collected over a period of time, forexample with daily granularity. For each time interval (e.g. a day), asingle point may be plotted on a two-dimensional graph with CTR_(O) onthe y axis and CTR_(P) on the x axis. A line may be fit to these points,and the slope of this line is the synergy. FIG. 5 provides a collectionof data used to calculate organic search CTR values and paid search CTRvalues. FIG. 6 illustrates a linear fit to the CTR values calculated inFIG. 5.

One benefit of the method for calculating a synergy score as describedin relation to the second embodiment is that it is possible to computethe synergy score based on arbitrary paid search advertising campaignchanges that the advertiser has carried out in the past, rather thanrequiring the advertiser to execute new changes to their marketingprogram purely for the purpose of computing a synergy score. In somecases with high enough query volume, it is possible to compute a synergyscore with as little as two days of data (rather than requiring datafrom a longer time period). The use of the search engine query volumedata normalizes many sources of website traffic volume variation (e.g.,weekly variations, seasonal variations and the impact of off-lineadvertising campaigns) that can otherwise confound the synergy scorecomputation.

One drawback of the method in relation to the first embodiment is thatthe search engine query volume data can introduce a new source of noise.Thus, a third embodiment assesses whether the line described in relationto plotting organic and paid search CTR values is satisfactory. Whetheror not the line is satisfactory can be determined using differenttechniques known in the art. For example, R² could be calculated. If theR² value exceeds a threshold value then the associated synergy score(i.e., slope of the line) is acceptable. Otherwise, the synergy score isrejected due to noise unaccounted for by the plot or the data collected.

Predictive Model for Estimating a Synergy Score

Given historical SERPs and synergy scores for an acceptable amount ofsearch engine queries, it is possible to determine a function thatestimates a synergy score for a newquery/search-engine/change-in-campaign combination based on otherparameters rather than measuring it directly. Parameters that may bepredictive of the synergy score include the rank of an advertiser's paidlisting on a SERP, the click through rate of a paid listing on a SERP,the total number of paid listings on a SERP, the number organic listingsof a website in the top N results on a SERP (where N is a small numbersuch as 5, 10, or 30), the sum of click through rates associated withthe advertiser's organic listings on a SERP, the rank of each organiclisting for a particular advertiser on a SERP and the semanticsimilarity of the text (“creative”) associated with an advertiser's paidand organic listings.

A function for estimating a synergy score based on the above parametersmay be designed by a person familiar with the problem space or may beautomatically determined from the historical data via a machine learningtechnique for function approximation such as linear regression, errorback propagation neural network learning or C4.5. A process of computinga synergy model via this method is illustrated via the diagram in FIG.7. A process of computing a synergy score via current data and a synergymodel is illustrated via the diagram in FIG. 8.

Even in cases where a predictive model is not used to generate a synergyscore for the computation of the total performance metrics (described inthe next section), the predictive model may be used to identify when itis necessary to update the synergy score by direct measurement or by thefit to CTR plot method. A significant discrepancy between the synergyscore estimated by the predictive model and the stored synergy scoreindicates that some aspect of the search engine results page has changedin a way that is significant for the paid/organic synergy. This aspectmay be the advertiser's organic rankings, a third party or competitorbidding on this search term, a change to a website of a third party orcompetitor or the advertiser's creative appearing with the paid searchad. Thus, the predictive model can be used to automatically identifysuch a discrepancy and initiate actions that compute a new synergyscore.

Performance Metrics that Quantify the Value of a Change to a Paid SearchCampaign

The synergy score defined above quantifies the extent to which paidsearch advertising cannibalizes traffic (and associated actions) thatwould otherwise have accrued via organic search listings if the score isnegative. The synergy score also quantifies the extent to which paidsearch advertising raises user awareness and drives additional trafficvia organic search listings if the score is positive.

Consequently, an accurate synergy score may be used to quantify theperformance of a paid search advertisement (e.g., the incrementaltraffic driven to a website beyond what would have accrued with theorganic listings only, and/or other performance metrics). Measuring theperformance of a paid search campaign will vary between advertisers andthe details of the campaign depending on how each advertiser regards itswebsite performance and its campaign.

For example, in a fourth embodiment, a return on investment (ROI) fromincremental traffic to a webpage may be determined based on thefollowing equation:ROI=(Value_(paid)*(1+Synergy Score)−Spend_(paid))/Spend_(paid),where Value_(paid) is the revenue resulting from paid clicks associatedwith a search query or queries, and Spend_(paid) is the total amountspent on advertising in relation to the search query or queries.

In a fifth embodiment, a cost per incremental value point may bedetermined based on the following equation:Cost per Incremental valuepoint=Spend_(paid)/(Value_(paid)*(1+Synergy)),where Value_(paid) is the number of value-based events resulting frompaid clicks. If the synergy score is −1 or less, there is no incrementalvalue and the result of the calculation may be displayed as INF.

In a sixth embodiment, a cost per incremental action may be determinedbased on the following equation:Cost per Incremental action=Spend_(paid)/(Value_(paid)*(1+Synergy)),where Value_(paid) is the number of action-based events resulting frompaid clicks. If the synergy score is −1 or less, there is no incrementalvalue and the result of the calculation may be displayed as INF.

The process of calculating a total value metric based on current dataand a synergy score is further illustrated via the flowchart in FIG. 9.

Search Engine Marketing Performance Optimization

The incremental performance metrics described above can be used toimprove the performance of a search engine advertising campaign invarious ways.

One approach is to sort keywords/search query by one or more incrementalperformance metrics. For example, FIG. 10 illustrates how keywords 1-10could be sorted based on Incrmental ROI. An advertiser can then decreasea maximum cost per click bid for keywords with the lowest incrementalperformance values or with incremental performance values below apredetermined threshold (among other approaches). Resultant marketingbudget savings could then be used to bid on new keywords or to raise themaximum cost per click for keywords with the high incremental ROI orwith an incremental ROI above a predetermined threshold (among otherapproaches).

Another approach is to use the incremental performance metric as thetarget of an optimization-based keyword bidding system. Such a biddingsystem automatically adjusts the bids subject to spending constraints inways that are somewhat similar to the manual process described above inrelation to FIG. 10.

Yet another approach is to use the synergy score to automaticallyidentify situations in which the text (creative) shown with a webpage ororganic listing can be improved. For example, if the synergy scorepredicted by a model built without variables based on the paid creativeis high, yet the measured synergy score is low, then it is likely thatthe branding value of the paid creative can be improved by extollingpositive features of their product or company relative to competitors.

By way of another example, if the synergy score is at or around 0.0, andone or more pages of a website are highly ranked in organic results(e.g., in the top five listings), and the paid ad CTR is high, then itis likely that the paid and organic listings appeal to differentsegments of the users issuing that query. Thus, it may be beneficial forthe advertiser to carry out search engine optimization to try to improvethe organic ranking of a landing page that appeals to the same usersthat click on the paid ad.

It will be apparent to one having ordinary skill in the art that avariety of other approaches may also be used.

Client Architecture

Various embodiments of the invention may be designed to operate oncomputer systems, servers, and/or other like devices. While the detailsof embodiments of the invention may vary and still be within the scopeof the claimed invention, FIG. 11 shows a block diagram depicting atypical network system 1100 for calculating a synergy score, creating apredictive model for estimating a synergy score and optimizing a paidsearch advertising campaign based on the calculated synergy score and/orthe predictive model. The network system 1100 is only one example of asuitable computing environment and is not intended to suggest anylimitation as to the scope of use or functionality of the invention.Neither should the network system 1100 be interpreted as having anydependency or requirement relating to any one or combination ofcomponents illustrated in the exemplary network system 1100.

Aspects of the invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer or server. Generally, program modules includeroutines, programs, objects, components, data structures, and the likethat perform particular tasks or implement particular abstract datatypes. The invention may also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices.

As is shown, the network system 1100 includes a communications network1110, such as the Internet or a private network, capable of providingcommunication between devices at search engines 1120, advertisers 1130,a synergy score analysis system 1140, and third party users 1150described hereinafter. The devices of FIG. 11 communicate with eachother via any number of methods known in the art, including wired andwireless communication pathways.

As shown in FIG. 11, a search engine 1120 (operating one or more servers1121 with one or more database 1123) is accessible by a third party user1150, an advertiser 1130, and by the analysis system 1140. The thirdparty user 1150 may utilize any number of computing devices that areconfigured to retrieve information from the World Wide Web (“WWW”), suchas a computer, a personal digital assistant (“FDA”), a mobile phone, atelevision or other network communications-enabled device. Theadvertiser 1130 is typically a business entity with one or more onlineor interactive marketing campaigns associated with the search engine1120. The analysis system 1140 operates one or more servers 1141 capableof Internet-based communication with the search engine 1120 and theadvertiser 1130. The analysis system 1140 includes a database 1143 whichmay be described as a hard disk drive for convenience, but this iscertainly not required, and one of ordinary skill in the art willrecognize that other storage media may be utilized without departingfrom the scope of the invention. In addition, one of ordinary skill inthe art will recognize that the database 1143, which is depicted forconvenience as a single storage device, may be realized by multiple(e.g., distributed) storage devices. One of skill in the art willfurther appreciate that the analysis system 1140 can be deployed at theadvertiser 1130.

As is discussed below, the analysis system 1140 enables the advertiser1130 to calculate a synergy score for a change to the advertiser's paidsearch advertising campaign. The analysis system 1140 further enablesthe advertiser 1130 to create a predictive model for estimating asynergy score. The advertiser 1130 can also use the analysis controlsystem 1140 to optimize (either manually or automatically) a paid searchadvertising campaign based on the calculated synergy score and/or thepredictive model.

As those skilled in the art will appreciate, various intermediarynetwork routing and other elements between the communication network1110 and the devices depicted in FIG. 11 have been omitted for the sakeof simplicity. Such intermediary elements may include, for example, thepublic-switched telephone network (“PSTN”), gateways or other serverdevices, wireless network devices, and other network infrastructureprovided by Internet service providers (“ISPs”).

Attention is now drawn to FIG. 12, which depicts an exemplaryimplementation of the advertiser 1130. As is shown, the advertiser 1130includes a server 1131 connected to a database 1133, both of which maycommunicate either directly or indirectly with the communication network1110. FIG. 12 also includes a computing device/system 1239 configured inaccordance with one implementation of the invention. The computingdevice 1239 may include, but not by way of limitation, a personalcomputer (PC), a personal digital assistant (PDA), a cell phone, atelevision (TV), etc., or any other device configured to send/receivedata to/from the communication network 1110, such as consumer electronicdevices and hand-held devices.

The implementation depicted in FIG. 12 includes a processor 1239 acoupled to ROM 1239 b, input/output devices 1239 c (e.g., a keyboard,mouse, etc.), a media drive 1239 d (e.g., a disk drive, USB port, etc.),a network connection 1239 e, a display 1239 f, memory 1239 g (e.g.,random access memory (RAM)), and a file storage device 1239 h.

The storage device 1239 h is described herein in several implementationsas a hard disk drive for convenience, but this is certainly notrequired, and one of ordinary skill in the art will recognize that otherstorage media may be utilized without departing from the scope of theinvention. In addition, one of ordinary skill in the art will recognizethat the storage device 1239 h, which is depicted for convenience as asingle storage device, may be realized by multiple (e.g., distributed)storage devices.

As shown, a software solution 1241 includes a data acquisition module1241 a, a synergy score calculation module 1241 b, a predictive modelgeneration module 1241 c and an paid search advertising campaignoptimization module 1241 d, all of which are implemented in software andare executed from the memory 1239 g by the processor 1239 a. Thesoftware 1241 can be configured to operate on personal computers (e.g.,handheld, notebook or desktop), servers or any device capable ofprocessing instructions embodied in executable code. Moreover, one ofordinary skill in the art will recognize that alternative embodiments,which implement one or more components in hardware, are well within thescope of the invention. Each module 1241 a-d functions similarly to therespective functionality described above in relation to collecting data,calculating a synergy score, determining a predictive model thatestimates a synergy score and quantify the value of a change to a paidsearch advertising campaign in order to optimize the paid searchadvertising campaign.

The exemplary systems and methods of the invention have been describedabove with respect to the analysis system 1140 and/or the advertiser1130. One of skill in the art will appreciate alternative embodimentswherein the functions of the analysis system 1140 are performed on otherdevices in the networked system 1100.

Those skilled in the art can readily recognize that numerous variationsand substitutions may be made in the invention, its use and itsconfiguration to achieve substantially the same results as achieved bythe embodiments described herein. Accordingly, there is no intention tolimit the invention to the disclosed exemplary forms. Many variations,modifications and alternative constructions fall within the scope andspirit of the disclosed invention as expressed in the claims.

1. A computer implemented method for determining an impact of a changeto a paid portion of a search advertising campaign that is associatedwith a website, comprising: identifying a first change made by anadvertiser to the paid portion of the search advertising campaign, thefirst change affecting a cost of the paid portion; determining, with afirst computing device, one or more effects of the first change, the oneor more effects being indicative of: a change in a volume of searchtraffic resulting from the paid portion, and a change in a volume ofsearch traffic resulting from an unpaid portion of the searchadvertising campaign; processing, with the first computing device or asecond computing device, the one or more determined effects to generatea first synergy score, wherein the first synergy score quantifies animpact of the first change on the volume of search traffic resultingfrom the unpaid portion; and storing the first synergy score in amachine readable memory in association with an indication of the firstchange.
 2. The method of claim 1, wherein the first change includes achange selected from the group consisting of a start of advertising forat least a first keyword, a stoppage of advertising for at least asecond keyword, a change to a maximum cost-per-click bid for at least athird keyword, a change to a budget limit for at least a fourth keywordand a change to a time period during which an advertisement is shown inrelation to at least a fifth keyword.
 3. The method of claim 1, wherethe one or more determined effects further include one or more effectsselected from the group consisting of: one or more changes to one ormore volumes of traffic from one or more organic listings, paid searchlistings or organic and paid search listings of the website at one ormore search engines, one or more changes to one or moreclick-through-rates associated with one or more organic listings, paidsearch listings or organic and paid search listings of the website atone or more search engines, one or more changes to one or more volumesof orders associated with one or more organic listings, paid searchlistings or organic and paid search listings of the website at one ormore search engines, one or more changes to revenue associated with oneor more organic listings, paid search listings or organic and paidsearch listings of the website at one or more search engines, one ormore changes to one or more number of page views associated with one ormore organic listings, paid search listings or organic and paid searchlistings of the website at one or more search engines, and one or morechanges to one or more whitepaper downloads associated with one or moreorganic listings, paid search listings or organic and paid searchlistings of the website at one or more search engines.
 4. The method ofclaim 3, wherein the processing the one or more determined effectsincludes comparing a difference between a volume of traffic from anorganic listing of the website at a search engine during a time periodexisting before the first change and a volume of traffic from an organiclisting of the website at the search engine during a time periodexisting after the first change to a difference between a volume oftraffic from a paid search listing of the website at the search engineduring the time period existing before the first change and a volume oftraffic from a paid search listing of the website at the search engineduring the time period existing after the first change.
 5. The method ofclaim 3, wherein the processing the one or more determined effectsincludes plotting, over a period of time, a relationship between aplurality of the one or more changes to the one or moreclick-through-rates associated with the one or more organic listings ofthe website at the one or more search engines and a correspondingplurality of the one or more changes to the one or moreclick-through-rates associated with the one or more paid search listingsof the website at the one or more search engines.
 6. The method of claim1, further comprising: generating a mathematical model for determiningan estimated synergy score based on the first synergy score, and storingthe mathematical model in a machine readable memory.
 7. The method ofclaim 6, wherein the generating a mathematical model for determining anestimated synergy score is further based on data selected from the groupconsisting of data representing one or more additional synergy scoresassociated with the paid portion of the search advertising campaign,data representing one or more synergy scores associated with one or moreother paid portions of other search advertising campaigns, datarepresenting one or more rankings of one or more websites in one or moreorganic listings at one or more search engines during one or moreperiods of time, data representing one or more rankings of the websitein one or more paid search listings at one or more search engines duringone or more periods of time, data representing one or moreclick-through-rates associated with one or more organic listings of thewebsite at one or more search engines, data representing one or moreclick-through-rates associated with one or more paid search listings ofthe website at one or more search engines, data representing structureof the website, data representing structure of one or more otherwebsites and data representing a semantic similarity of text associatedwith one or more organic listings of one or more websites at one or moresearch engines and text associated with one or more paid search listingsof the website at one or more search engines.
 8. The method of claim 6,wherein the mathematical model is generated using linear regressiontechnique.
 9. The method of claim 6, wherein the mathematical model isgenerated using neural network technique.
 10. The method of claim 6,further comprising: estimating, using mathematical model, an estimatedsynergy score.
 11. The method of claim 10, further comprising: comparingthe first synergy score to the estimated synergy score; and responsiveto the comparing, generating a second synergy score.
 12. The method ofclaim 1, further comprising: determining a value of the first changebased at least in part on the first synergy score, a revenue associatedwith the paid portion, and a cost associated with the paid portion. 13.The method of claim 12, wherein the value is determined by dividing therevenue by the cost to generate a resulting quotient and multiplying theresulting quotient by a scaling factor based on the first synergy score.14. The method of claim 12, wherein the value is determined by dividingthe cost by the revenue to generate a resulting quotient and multiplyingthe resulting quotient by a scaling factor based on the first synergyscore.
 15. The method of claim 12, further comprising: determining,based on the value, an adjustment to be made to the paid portion. 16.The method of claim 1 wherein the processing includes generating a firstsynergy score based at least in part upon a relationship (R) comprising:R=(TO2−TO1)/(TP2−TP1) wherein TO1 represents volume of search trafficfrom the unpaid portion before the first change, TO2 represents volumeof search traffic from the unpaid portion after the first change, TP1represents volume of search traffic from the paid portion before thefirst change, TP2 represents volume of search traffic from the paidportion after the first change.
 17. A non-transitory computer readablemedium storing computer executable instructions which when executed on acomputer simulate a process, the instructions comprising instructionsto: identify a first change made by an advertiser to a paid portion ofthe search advertising campaign, the first change affecting a cost ofthe paid portion; determine one or more effects of the first change, theone or more effects being indicative of: a change in a volume of searchtraffic resulting from the paid portion, and a change in a volume ofsearch traffic resulting from an unpaid portion of the searchadvertising campaign; process the one or more determined effects togenerate a first synergy score, wherein the first synergy scorequantifies an impact of the first change on the volume of search trafficresulting from the unpaid portion; and store the first synergy score ina machine readable memory in association with an indication of the firstchange.
 18. The computer readable medium of claim 17, wherein the firstchange includes a change selected from the group consisting of a startof advertising for at least a first keyword, a stoppage of advertisingfor at least a second keyword, a change to a maximum cost-per-click bidfor at least a third keyword, a change to a budget limit for at least afourth keyword, and a change to a time period during which anadvertisement is shown in relation to at least a fifth keyword.
 19. Thecomputer readable medium of claim 17, wherein the one or more determinedeffects further include one or more effects selected from the groupconsisting of: one or more changes to one or more volumes of trafficfrom one or more organic listings, paid search listings or organic andpaid search listings of the website at one or more search engines, oneor more changes to one or more click-through-rates associated with oneor more organic listings, paid search listings or organic and paidsearch listings of the website at one or more search engines, one ormore changes to one or more volumes of orders associated with one ormore organic listings, paid search listings or organic and paid searchlistings of the website at one or more search engines, one or morechanges to revenue associated with one or more organic listings, paidsearch listings or organic and paid search listings of the website atone or more search engines, one or more changes to one or more number ofpage views associated with one or more organic listings, paid searchlistings or organic and paid search listings of the website at one ormore search engines, and one or more changes to one or more whitepaperdownloads associated with one or more organic listings, paid searchlistings or organic and paid search listings of the website at one ormore search engines.
 20. The computer readable medium of claim 19,wherein the instructions to process the one or more determined effectsincludes instructions to compare a difference between a volume oftraffic from an organic listing of the website at a search engine duringa time period existing before the first change and a volume of trafficfrom an organic listing of the website at the search engine during atime period existing after the first change to a difference between avolume of traffic from a paid search listing of the website at thesearch engine during the time period existing before the first changeand a volume of traffic from a paid search listing of the website at thesearch engine during the time period existing after the first change, orto plot, over a period of time, a relationship between a plurality ofthe one or more changes to the one or more click-through-ratesassociated with the one or more organic listings of the website at theone or more search engines and a corresponding plurality of the one ormore changes to the one or more click-through-rates associated with theone or more paid search listings of the website at the one or moresearch engines.
 21. The computer readable medium of claim 17, whereinthe instructions further comprise instructions to: generate amathematical model for determining an estimated synergy score based onthe first synergy score and data selected from the group consisting ofdata representing one or more additional synergy score associated withthe paid portion of the search advertising campaign, data representingone or more synergy scores associated with one or more other paidportions of other search advertising campaigns, data representing one ormore ranking of one or more websites in one or more organic listings atone or more search engines during one or more periods of time, datarepresenting one or more ranking of the website in one or more paidsearch listings at one or more search engines during one or more periodsof time, data representing one or more click-through-rates associatedwith one or more organic listings of the website at one or more organiclistings of the website at one or more search engines, data representingone or more click-through-rates associated with one or more paid searchlistings of the website at one or more search engines, data representingstructure of the website, data representing structure of one or moreother websites and data representing a sematic similarity of textassociated with one or more organic listings of one or more websites atone or more search engines and text associated with one or more paidsearch listings of the website at one or more search engine; and storethe mathematical model in a machine readable memory.
 22. The computerreadable medium of claim 21, wherein the mathematical model is generatedusing a linear regression technique or neural network technique.
 23. Thecomputer readable medium of claim 21, wherein the instructions furthercomprise instructions to: estimate, using the mathematical model, anestimated synergy score.
 24. The computer readable medium of claim 23,wherein the instructions further comprise instructions to: compare thefirst synergy score to the estimated synergy score; and responsive tothe comparison, generate a second synergy score.
 25. The computerreadable medium of claim 17, wherein the instructions further compriseinstructions to: determine a value of the first change based at least inpart on the first synergy score, a revenue associated with the paidportion, and a cost associated with the paid portion.
 26. The computerreadable medium of claim 25, wherein the value is determined by dividingthe revenue by the cost to generate a resulting first quotient andmultiplying the resulting first quotient by a scaling factor based onthe first synergy score or by dividing the cost by the revenue togenerate a resulting second quotient and multiplying the resultingsecond quotient by a scaling factor based on the first synergy score.27. The computer readable medium of claim 25, wherein the instructionsfurther comprise instructions to: determine, based on the value, anadjustment to be made to the paid portion.
 28. The computer readablemedium of claim 17, wherein the processing includes generating a firstsynergy score based at least in part upon a relationship (R) comprising:R=(TO2−TO1)/(TP2−TP1) wherein TO1 represents volume of search trafficfrom the unpaid portion before the first change, TO2 represents volumeof search traffic from the unpaid portion after the first change, TP1represents volume of search traffic from the paid portion before thefirst change, TP2 represents volume of search traffic from the paidportion after the first change.
 29. A system for measuring an impact ofa change to a paid portion of a search advertising campaign that isassociated with a website, comprising: at least one processor; a networkinterface; a memory, operatively coupled to the processor, for storinglogical instructions wherein execution of the logical instruction by theprocessor results in performing of at least the following operations:identifying a first change made by an advertiser to the paid portion ofthe search advertising campaign, the first change affecting a cost ofthe paid portion; determining one or more effects of the first change,the one or more effects being indicative of: a change in a volume ofsearch traffic resulting from the paid portion, and a change in a volumeof search traffic resulting from an unpaid portion of the searchadvertising campaign; processing the one or more determined effects togenerate a first synergy score, wherein the first synergy scorequantifies an impact of the first change on the volume of search trafficresulting from the unpaid portion; and storing the first synergy scorein a machine readable memory in association with an indication of thefirst change.
 30. The system of claim 29, wherein the first changeincludes a change selected from the group consisting of a start ofadvertising for at least a first keyword, a stoppage of advertising forat least a second keyword, a change to a maximum cost-per-click bid forat least a third keyword, a change to a budget limit for at least afourth keyword, and a change to a time period during which anadvertisement is shown in relation to at least a fifth keyword.
 31. Thesystem of claim 29, wherein the one or more determined effects furtherincludes one or more effects selected from the group consisting of: oneor more changes to one or more volumes of traffic from one or moreorganic listings, paid search listings or organic and paid searchlistings of the website at one or more search engines, one or morechanges to one or more click-through-rates associated with one or moreorganic listings, paid search listings or organic and paid searchlistings of the website at one or more search engines, one or morechanges to one or more volumes of orders associated with one or moreorganic listings, paid search listings or organic and paid searchlistings of the website at one or more search engines, one or morechanges to revenue associated with one or more organic listings, paidsearch listings or organic and paid search listings of the website atone or more search engines, one or more changes to one or more number ofpage views associated with one or more organic listings, paid searchlistings or organic and paid search listings of the website at one ormore search engines, and one or more changes to one or more whitepaperdownloads associated with one or more organic listings, paid searchlistings or organic and paid search listings of the website at one ormore search engines.
 32. The system of claim 31, wherein the processingthe one or more determined effects includes comparing a differencebetween a volume of traffic from an organic listing of the website at asearch engine during a time period existing before the first change anda volume of traffic from an organic listing of the website at the searchengine during a time period existing after the first change to adifference between a volume of traffic from a paid search listing of thewebsite at the search engine during the time period existing before thefirst change and a volume of traffic from a paid search listing of thewebsite at the search engine during the time period existing after thefirst change, or to plotting, over a period of time, a relationshipbetween a plurality of the one or more changes to the one or moreclick-through-rates associated with the one or more organic listings ofthe website at the one or more search engines and a correspondingplurality of the one or more changes to the one or moreclick-through-rates associated with the one or more paid search listingsof the website at the one or more search engines.
 33. The system ofclaim 29, wherein the execution of the logical instructions by theprocessor resulting in the performing of at least the followingadditional operations: generating a mathematical model for determiningan estimated synergy score based on the first synergy score; and storingthe mathematical model in a machine readable memory.
 34. The system ofclaim 33, wherein the generating a mathematical model for determining anestimated synergy score is further based on data selected from the groupconsisting of data representing one or more additional synergy scoreassociated with the paid portion of the search advertising campaign,data representing one or more synergy scores associated with one or moreother paid portions of other search advertising campaigns, datarepresenting one or more ranking of one or more websites in one or moreorganic listings at one or more search engines during one or moreperiods of time, data representing one or more rankings of the websitein one or more paid search listings at one or more search engines duringone or more periods of time, data representing one or moreclick-through-rates associated with one or more organic listings of thewebsite at one or more organic listings of the website at one or moresearch engines, data representing one or more click-through-ratesassociated with one or more paid search listings of the website at oneor more search engines, data representing structure of the website, datarepresenting structure of one or more other websites and datarepresenting a sematic similarity of text associated with one or moreorganic listings of one or more websites at one or more search enginesand text associated with one or more paid search listings of the websiteat one or more search engine.
 35. The system of claim 33, wherein themathematical model is generated using a linear regression technique or aneural network technique.
 36. The system of claim 33, wherein theexecution of the logical instructions by the processor results in theperforming of at least the following additional operations: estimating,using the mathematical model, an estimated synergy score.
 37. The systemof claim 36, wherein the execution of the logical instructions by theprocessor results in the performing of at least the following additionaloperations: comparing the first synergy score to the estimated synergyscore; and responsive to the comparing, generating a second synergyscore.
 38. The system of claim 29, wherein the execution of the logicalinstructions by the processor results in the performing of at least thefollowing additional operations: determining a value for the firstchange based at least in part on the first synergy score, a revenueassociated with the paid search campaign, and a cost associated with thepaid search campaign.
 39. The system of claim 38, where the value isdetermined by dividing the revenue by the cost to generate a resultingfirst quotient and multiplying the resulting first quotient by a scalingfactor based on the first synergy score or by dividing the cost by therevenue to generate a resulting second quotient and multiplying theresulting second quotient by a scaling factor based on the first synergyscore.
 40. The system of claim 38, wherein the execution of the logicalinstructions by the processor results in the performing of at least thefollowing additional operation: determining, based on the value, anadjustment to be made to the paid portion.
 41. The system of claim 29,wherein the processing includes generating a first synergy score basedat least in part upon a relationship (R) comprising:R=(TO2−TO1)/(TP2−TP1) wherein TO1 represents volume of search trafficfrom the unpaid portion before the first change, TO2 represents volumeof search traffic from the unpaid portion after the first change, TP1represents volume of search traffic from the paid portion before thefirst change, TP2 represents volume of search traffic from the paidportion after the first change.
 42. A system, comprising: at least oneprocessor; a network interface; one or more memories, operativelycoupled to the processor, for storing logical instructions whereinexecution of the logical instructions by the processor results in theperforming of at least the following operations: receiving, through thenetwork interface during a first period of time, first traffic data andsecond traffic data measured by a Web analytic system wherein the firsttraffic data is indicative of search traffic to a website resulting froma paid portion of a search advertising campaign and the second trafficdata is indicative of search traffic to the website resulting from anunpaid portion of the search advertising campaign; making a change tothe paid portion of the search advertising campaign in order to create amodified advertising campaign, the change being initiated by anadvertiser and affecting a cost of the paid portion; receiving, during asecond period of time, third traffic data and fourth traffic datawherein the third traffic data is indicative of search trafficassociated with a paid portion of the modified advertising campaign andthe fourth traffic data is indicative of search traffic associated withan unpaid portion of the modified advertising campaign; generating asynergy score based upon the at least first traffic data, the secondtraffic data, the third traffic data and the fourth traffic data,wherein the synergy score quantifies an impact of the change on the atleast one of a volume of traffic to the website resulting from the paidportions and the value of the traffic to the website resulting from theunpaid portions; and storing the synergy score in the one or morememories in association with an indication of the change.
 43. The systemof claim 42 wherein the generating includes determining the synergyscore based at least in part upon a relationship (R) comprising:R=(TO2−TO1)/(TP2−TP1) wherein TO1 represents the second traffic data,TO2 represents the fourth traffic data, TP1 represents the first trafficdata and TP2 represents the third traffic data.
 44. The system of claim42 wherein the volume of traffic comprises traffic arising from unpaidsearch listings.
 45. The system of claim 42 wherein the change comprisesbeginning advertising for at least a first keyword.
 46. The system ofclaim 45 wherein the change further comprises discontinuing advertisingfor at least a second keyword.
 47. The system of claim 42 wherein thechange comprises discontinuing advertising for at least one keyword. 48.The system of claim 42 wherein the change comprises modifying a maximumcost-per-click bid for at least one keyword.
 49. The system of claim 42wherein the change comprises modifying a change to a budget limit for atleast one keyword.
 50. The system of claim 42 wherein the changecomprises modifying a time period during which an advertisement is shownin relation to at least one keyword.